2024-12-23-monedero24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
RADR: A Robust Domain-Adversarial-based Framework for Automated Diabetic Retinopathy Severity Classification	Diabetic retinopathy (DR), a potentially vision-threatening condition, necessitates accurate diagnosis and staging, which deep-learning models can facilitate. However, these models often struggle with robustness in clinical practice due to distribution shifts caused by variations in data acquisition protocols and hardware. We propose RADR, a novel deep-learning framework for DR severity classification, aimed at generalization across diverse datasets and clinic cameras. Our work builds upon existing research: we combine several ideas to perform extensive dataset curation, preprocessing, and enrichment with camera information. We then use a domain adversarial training regime, which encourages our model to extract features that are both task-relevant and invariant to domain shifts. We explore our framework in its various levels of complexity in combination with multiple data augmentations policies in an ablative fashion. Experimental results demonstrate the effectiveness of our proposed method, achieving competitive performance to multiple state-of-the-art models on three unseen external datasets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	monedero24a	0	RADR: A Robust Domain-Adversarial-based Framework for Automated Diabetic Retinopathy Severity Classification	1026	1039	1026-1039	1026	false	Monedero, Sara M{\'\i}nguez and Westhaeusser, Fabian and Yaghoubi, Ehsan and Frintrop, Simone and Zimmermann, Marina	given family Sara Mı́nguez Monedero given family Fabian Westhaeusser given family Ehsan Yaghoubi given family Simone Frintrop given family Marina Zimmermann	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/monedero24a/monedero24a.pdf